Effects of weather on temperatures of the grain bin components and headspace of a 10-m diameter corrugated steel bin.
Bibliographic record
Abstract
The mean global temperatures are increasing as a result of climate change. To understand how the change in ambient weather influences the temperature of the stored grain, the temperature fluctuation patterns of the floor, roof, sidewalls, and headspace were monitored from mid-August 2019 to the end of October 2021 in Winnipeg, Canada. The bin was filled with 300 t of wheat at an initial average moisture content of 12.5 ± 0.1% (wet basis). The thermocouples were installed at 17, 9, and 12 locations on the floor, roof (outside), and sidewalls (outside) of the bin, respectively. Sixteen temperature and relative humidity sensors were installed at different locations with varying distances from the surface of the grain in the headspace. The ambient weather (air temperature (°C), relative humidity (%), barometric pressure (kPa), average solar radiation (W/m2), precipitation (mm), wind speed (m/s), and wind direction (degrees with reference to the north)) were also measured near the bin during the study period. The temperatures of the roof, sidewalls, and headspace were influenced by the ambient temperature and solar radiation. In Year II (November 2020 – October 2021), the floor, roof, sidewalls, and headspace temperatures were higher by 2.1 ± 0.1°C, 3.9 ± 0.1°C, 3.5 ± 0.2°C, and 1.9 ± 0.1°C than that in Year I (November 2019 - October 2020), respectively. The ambient temperature increased by 1.8°C in year II, compared to year I. These results can be used in the prediction of temperatures in grain bins caused by weather changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".